Tag Archives: digital analytics

Visualizing the customer journey in the context of the store is the foundation for analyzing in-store data. The metrics and the store context provide a framework for translating customer measurement data into something that is immediately understandable as a shopper’s journey. But visualizing information is just the first step in making it actionable. Understanding the data is, of course, essential. But you can understand data quite well and still have no idea what to do with it. In fact, that’s a problem we see all the time with analytics. And while it’s a problem that no technology solution can solve entirely (since there are always business and organizational issues to be tackled), there are analytic and reporting techniques that can really help. We’ve built a number of them into DM1, starting with in-store funnel analytics.

The idea behind a conversion funnel is simple. The customer journey is chopped up into discrete steps based on increasing likelihood to purchase. If we analyze the journey by those discrete steps, we can work to optimize the flow from one step to the next. Improve the flow between any funnel step and the next, and the chance is excellent that you’ll improve the overall funnel conversion as well. Funnels give you a specific place to start. They let you figure out which parts of the overall customer journey are already working well and which aren’t. They let you focus on specific areas with the confidence that if you can improve performance you’ll make a significant difference. And they make it possible to easily measure success. All you have to measure is the number of people moving from one step to the next.

Funnels are THE paradigm for analytics and optimization in eCommerce. In fact, it was largely on their ability to help merchants understand and improve eCommerce funnels that digital analytics solutions first gained traction. And to this day, eCommerce testing and analytics practitioners almost always work by breaking down the customer journey into funnel steps and then working to optimize each step. While the measurement of funnels is itself interesting, I think the real value in funnel analysis is the process it supports. That ability to target specific aspects of the journey, figure out which ones are the most broken, and then test possible improvements is at the heart of so much of the continuous improvement that makes digital players successful.

One of our big goals with Digital Mortar is to bring the in-store funnel paradigm and the discipline of continuous improvement to the store. DM1 delivers on the technology and analytic part of that program.

With DM1, you can start a funnel at any place in the store and at any stage in the customer journey. But the most natural place to start is with a shopper entering the store. As you can see, DM1 lets you choose any area of the store you’ve defined and lets you pick from a range of engagement metrics.

Nearly 84 thousand shoppers entered the store in October. Since that’s where the measurement starts, this first step of the funnel doesn’t have any fallout. Everyone I measured, by definition, entered the store. It’s worth noting – and I get asked this a lot – that you CAN track pass-by traffic if you setup the measurement system appropriately. Doing so allows you to extend the funnel outside the store!

I could build a store-wide funnel, looking at conversion across the whole store. But it’s usually more interesting and actionable to focus a bit. So my funnel is going to focus on a specific section of the store – Team Gear.

Adding “Visits to Team Gear” to the funnel, I can see that around 15 thousand shoppers – about 18% of store visitors – visited Team Gear. It took the average visitor about 2 minutes before entry to reach Team Gear. Which makes sense because this area is pretty front of store

But one of the real complexities to in-store measurement is that since shoppers are navigating a physical environment they often pass-thru areas without being interested in them. That doesn’t happen much in digital.

I want to know how many people SHOPPED in Team Gear out of the folks who had the opportunity. And I can see that by selecting Lingers as my metric in the next funnel step. These last two steps illustrate a powerful metric in store measurement that’s simply never been available before. Stores have been able to measure conversion (checkouts/door entries) at the macro level, but at the area level this gets reduced to sales per square foot.

That isn’t reflective of the real opportunity a square foot provides. By measuring where shoppers actually WENT and where they SHOPPED, we have a real KPI of how well a section is performing given its opportunity.

Only about 1 in 7 shoppers who passed through Team Gear actually Shopped there. That’s a problem I’d probably want to tackle.

From here, I can add Fitting Room and CashWrap to the funnel. At every step along the way I can see how many shoppers I’m losing from the total opportunity. I can also see how much time is passing and how many stops the shopper made in-between.

In the end, I have a customer funnel for Team Gear that runs from Store Entry to Cash-Wrap that looks like this:

Any start place. Any level of engagement. Any steps in between. DM1 builds the funnels you need to support analytics and testing.

Pretty cool.

There’s no doubt in my mind that the picture of the shopper journey that DM1 provides drives better understanding. But as I said earlier, analytics isn’t improvement. It’s a way to drive improvement.

The funnel paradigm works less because of it’s analytics potential than because of the process it helps define. In-store funnels focus optimization efforts and make them easily measurable. Whether I tackle the step with the highest abandonment rate, try to build the initial opportunity, or attempt to remove distractions between key steps, funnel analysis helps guide my reasoning about what to test in the store and provides a fully baked way to measure whether store changes drove the desired behavior.

Driving real value with analytics is much harder than people assume. Doing it well requires solving two separate, equally thorny problems. The first – fairly obvious problem – is being able to use data to deepen your understanding of important business questions. That’s what analytics is all about. The second problem is being able to use that understanding to drive business change. Affecting change is a political/operational problem that’s often every bit as difficult as doing the actual analysis. Most people have a hard time understanding what the data means and are reluctant to change without that understanding. So, giving analysts tools that help describe and contextualize the data in a way that’s easy to understand is a double-edged sword in the best of ways – it helps solves two problems. It helps the analyst use the data and it helps the analyst EXPLAIN the data to others more effectively. That’s why having a rich, powerful, UNDERSTANDABLE set of store metrics is critical to analytic success with in-store customer tracking.

Some kinds of data are very intuitive for most of us. We all understand basic demographic categories. We understand the difference between young and old. Between men and women. We live those data points on a daily basis. But behavioral data has always been more challenging. When I first started using web analytics data, the big challenge was how to make sense of a bunch of behaviors. What did it mean that someone viewed 7 pages or spent 4.5 minutes on a Website? Well, it turned out that it didn’t mean much at all. The interesting stuff in web analytics wasn’t how many pages a visitor had consumed – it was what those pages were about. It meant something to know that a visitor to a brokerage site clicked on a page about 529 accounts. It meant they had children. It meant they were interested in 529 accounts. And depending on what 529 information they chose to consume, it might indicate they were actively comparing plans or just doing early stage research. And the more content someone consumed, the more we knew about who they were and what they cared about.

Which was what we needed to optimize the experience. To personalize. To surface the right products. With the right messages. At the right time. Knowing more about the customer was the key to making analytics actionable and finding the right way to describe the behavior with data was the key to using analytics effectively.

So when it comes to in-store customer measurement, what kind of data is meaningful? What’s descriptive? What helps analysts understand? What helps drive action?

The answer, it turns out, isn’t all that different from what works in the digital realm. Just as the key to understanding a web visit turns out to be understanding the content a visitor selected and consumed, the key to understanding a store visit turns out to be understanding the store. You have to know what the shopper looked at. What was there when they stopped and lingered. What was along the corridor that they traversed but didn’t shop. You have to know the fitting room from the cash-wrap and an endcap from an aisle and you have to know what products were there. What’s more, you have to place the data in that context.

Here’s what the data from an in-store measurement collection system looks like in its raw form, frame by frame:

Time

X

Y

04:06.0

35

60

06:50.0

9

66

09:10.0

23

74

11:02.0

18

92

11:35.0

33

98

13:15.0

28

74

14:25.0

7

81

16:16.0

41

75

19:09.0

49

62

21:03.0

45

72

23:23.0

55

83

23:58.0

54

90

24:09.0

40

86

25:05.0

15

90

27:24.0

7

79

27:45.0

43

99

28:42.0

37

97

29:25.0

45

80

32:07.0

47

75

33:05.0

16

77

35:31.0

37

65

36:08.0

34

75

36:33.0

9

73

39:16.0

35

76

40:07.0

13

97

That’s a visit to a store. A little challenging to make sense of, right?

It’s our job to translate that into a journey with the necessary context to make the data useful.

That starts by mapping the data onto the store:

By overlaying the measurement frames, we can distinguish the path the user took through the store:

With simple analysis of the frames, we can figure out where and when a customer shifted from navigating the store to actually spending time. And that first place the shopper actually spends time, has special significance for understanding who they are.

In DM1, the first shopping point is marked as the DRAW. It’s where the shopper WENT FIRST in the store:

In this case, Customer Service was the Draw – indicating that this shopping visit is a return or in-store pickup. But the visit didn’t end there.

Following the journey, we can see what else the customer was exposed to and where else they actually spent time and shopped. In DM1, we capture each place the shopper spent time as a LINGER:

Lingers tell us about opportunity and interest. These are the things the shopper cared about and might have purchased.

But not every linger is created equal. In some places, the shopper might spend significantly more time – indicating a higher level of engagement. In DM1, these locations are called out on the journey as CONSIDERS:

Having multiple levels of shopper engagement lets DM1 create a more detailed picture of the shopper and a better in-store funnel. Of course, one of the keys to understanding the in-store funnel is knowing when a shopper interacts with an Associate. That’s a huge sales driver (and a huge driver – positive or negative – to customer experience). In DM1, we track the places where a shopper talked with and Associate as INTERACTIONS. They’re a key part of the journey:

Of course, you also want to know when/if a customer actually purchased. We track check-outs as CONVERSIONS – and have the ability to do that regardless of whether it’s a traditional cash-wrap or a distributed checkout environment:

Since we have the whole journey, we can also track which areas a customer shopped prior to checkout and we’ve created two measures for that. One is the area shopped directly before checkout (which is called the CONVERSION DRIVER) and the other captures every area the customer lingered prior to checkout – called ATTRIBUTED CONVERSIONS.

To use measurement effectively, you have to be able to communicate what the numbers mean. For the in-store journey, there simply isn’t a standardized way of talking about what customers did. With DM1, we’ve not only captured that data, we’ve constructed a powerful, working language (much of it borrowed from the digital realm) that describes the entire in-store funnel.

From Visits (shopper entering store), to Lingers (spending time in an area), to Consideration (deeper engagement), to Investment (Fitting Rooms, etc.), to Interactions (Associate conversations) to Conversion (checkout) along with metrics to indicate the success of each stage along the way. We’ve even created the metric language for failure points. DM1 tracks where customers Lingered and then left the store without buying (Exits) and even visits where the shopper only lingered in one location before exiting (Bounces).

Having a rich set of metrics and a powerful language for describing the customer journey may seem like utter table-stakes to folks weaned on digital analytics. But it took years for digital analytics tools to offer a mature and standardized measurement language. In-store tracking hasn’t had anything remotely similar. Most existing solutions offer two basic metrics (Visits and Dwells). That’s not enough for good analytics and it’s not a rich enough vocabulary to even begin to describe the in-store journey.

DM1 goes a huge mile down the road to fixing that problem.

[BTW – if you want to see how DM1 Store Visualization actually works, check out these live videos of DM1 in Action]

The equation in retail today is simple. Evolve or die. But if analytics is one of the core tools to drive successful evolution, we have a problem. From an analytics perspective, we’re used to a certain view of the store. We know how many shoppers we get (door counting) and we know what we sold. We know how many Associates we had. We (may) know what they sold. This isn’t dog food. If you had to pick a very small set of metrics to work with to optimize the store, most of these would belong. But we’re missing a lot, too. We’re missing almost any analytic detail around the customer journey in the store. That’s a particularly acute lack (as I noted in my last post) in a world where we’re increasingly focused on delivering (and measuring) better store experiences. In a transaction-focused world, transactions are the key measures. In an experience world? Not so much. So journey measurement is a critical component of today’s store optimization. And there’s the problem. Because the in-store measurement systems we have available are tragically limited. DM1, our new platform, is designed to fix that problem.

People like to talk about analytics as if it just falls out of data. As if analysts can take any data set and any tool and somehow make a tasty concoction. It isn’t true. Analytics is hard work. A really great analyst can work wonders, but some data sets are too poor to use. Some tools lock away the data or munge it beyond recognition. And remember, the most expensive part of analytics is the human component. Why arm those folks with tools that make their job slow and hard? Believe me, when it comes to getting value out of analytics, it’s hard enough with good tools and good data. You can kid yourself that it’s okay to get by with less. But at some point you’re just flushing your investment and your time away. In two previous posts, I called out a set of problems with the current generation of store customer measurement systems. Sure, every system has problems – no analytics tool is perfect. But some problems are much worse than others. And some problems cripple or severely limit our ability to use journey data to drive real improvement.

When it comes to store measurement tools, here are the killers: lack of segmentation, lack of store context, inappropriate analytics tools, inability to integrate Associate data and interactions, inability to integrate into the broader analytics ecosystem and an unwillingness to provide cleaned, event-level data that might let analysts get around these other issues.

Those are the problems we set out to solve when we built DM1.

Let’s start with Segmentation. Segmentation can sound like a fancy add-on. A nice to have. Important maybe, but not critical.

That isn’t right. Marketing analytics just is segmentation. There is no such thing as an average customer. And when it comes to customer journey’s, trying to average them makes them meaningless. One customer walks in the door, turns around and leaves. Another lingers for twenty minutes shopping intensively in two departments. Averaging the two? It means nothing.

Almost every analysis you’ll do, every question you’ll try to answer about store layout, store merchandising, promotion performance, or experience will require you to segment. To be able to look at the just the customers who DID THIS. Just the customers who experienced THAT.

Think about it. When you build a new experience, and want to know how it changed behavior you need to segment. When you change a greeting script or adjust a presentation and want to know if it improved store performance you need segmentation. When you change Associate interaction strategies and want to see how it’s impacting customer behavior you need segmentation. When you add a store event and want to see how it impacted key sections, you need segmentation. When you want to know what other stuff shoppers interested in a category cared about, you need segmentation. When you want to know how successful journeys differed from unsuccessful ones, you need segmentation. When you want to know what happens with people who do store pickup or returns, you need segmentation.

In other words, if you want to use customer journey tracking tools for tracking customer journeys, you need segmentation.

If your tool doesn’t provide segmentation and it doesn’t give the analyst access to the data outside it’s interface, you’re stuck. It doesn’t matter how brilliant you are. How clever. Or how skilled. You can’t manufacture segmentation.

Why don’t most tools deliver segmentation?

If it’s so important, why isn’t it there? Supporting segmentation is actually kind of hard. Most reporting systems work by aggregating the data. They add it up by various dimensions so that it can be collapsed into easily accessible chunks delivered up into reports. But when you add segmentation into the mix, you have to chunk every metric by every possible combination of segments. It’s messy and it often expands the data so much that reports take forever to run. That’s not good either.

We engineered DM1 differently. In DM1, all the data is stored in memory. What does that mean? You know how on your PC, when you save something to disk or first load it from the hard drive it takes a decent chunk of time? But once it’s loaded everything goes along just fine? That’s because memory is much faster than disk. So once your PowerPoint or spreadsheet is loaded into memory, things run much faster. With DM1, your entire data set is stored in-memory. Every record. Every journey. And because it’s in-memory, we can pass all your data for every query, really fast. But we didn’t stop there. When you run a query on DM1, that query is split up into lots of chunks (called threads) each of which process its own little range of data – usually a day or two. Then they combine all the answers together and deliver them back to you.

That means that not only does DM1 deliver reports almost instantaneously, it means we can run even pretty complex queries without pre-aggregating anything and without having to worry about the performance. Things like…segmentation.

Segmentation and DM1

In DM1, you can segment on quite a few different things. You can segment on where in the store the shopper spent time. You can segment on how much time they spent. You can segment on their total time in the store. You can segment on when they shopped (both by day of week and time of day). You can segment on whether they purchased or not. And even whether they interacted with an Associate.

If, for example, you want to understand potential cross-sells, you can apply a segment that selects only visitors who spent a significant amount of time shopping in a section or department. Actually, this undersells the capability because it’s in no way limited to any specific type of store area. You can segment on any store area down to the level of accuracy achieved by the collection architecture.

What’s more, DM1 keeps track of historical meta-data for every area of the store. Meaning that even if you changed, moved or re-sized an area of the store, DM1 still tracks and segments on it appropriately.

So if you want to see what else shoppers who looked at, for example, Jackets also considered, you can simply apply the segmentation. It will work correctly no matter how many times the area was re-defined. It will work even in store roll-ups with fundamentally different store types. And with the segment applied, you can view any DM1 visualization, chart or table. So you can look at where else Jacket Shoppers passed through, where they lingered, where they engaged more deeply, what else they were likely to buy, where they exited from, where they went first, where they spent the most time, etc. etc. You can even answer questions such as whether shoppers in Jackets were more or less likely to interact with Sales Associates in that section or another.

Want to see if Jacket shoppers are different on weekdays and weekends? If transactors are different from browsers? If having an Associate interaction significantly increases browse time? Well, DM1 let’s you stack segments. So you can choose any other filter type and apply it as well. I think the Day and Time part segmentation’s are particularly cool (and unusual). They let you seamlessly focus on morning shoppers or late afternoon, weekend shoppers or even just shoppers who come in over lunchtime. Sure, with door-counting you know your overall store volume. But with day and time-part segmentation you know volume, interest, consideration, and attribution for every measured area of the store and every type of customer for every hour and day of week.

DM1’s segmentation capability makes it easy to see whether merchandise is grouped appropriately. How different types of visitor journeys play out. Where promotional opportunities exist. And how and where the flow of traffic contradicts the overall store layout or associate plan. For identified shoppers, it also means you can create extraordinarily rich behavioral profiles that capture in near real-time what a shopper cares about right now.

It comes down to this. Without segmentation, analytics solutions are just baby toys. Segmentation is what makes them real marketing tools.

The Roadmap

DM1 certainly delivers far more segmentation than any other product in this space. But it’s still quite a bit short of what I’d like to deliver. I mean it when I say that segmentation is the heart and soul of marketing analytics. A segmentation capability can never be too robust.

Not only do we plan to add even more basic segmentation options to DM1, we’ve also roadmapped a full segmentation builder (of the sort that the more recent generation of digital analytics tools include). Our current segmentation interface is simple. Implied “ors” within a category and implied “ands” across segmentation types. That’s by far the most common type of segmentation analysts use. But it’s not the only kind that’s valuable. Being able to apply more advanced logic and groupings, customized thresholds, and time based concepts (visited before / after) are all valuable for certain types of analysis.

I’ve also roadmapped basic machine learning to create data-driven segmentations and a UI that provides a more persona-based approach to understanding visitor types and tracking them as cohorts.

The beauty of our underlying data structures is that none of this is architecturally a challenge. Creating a good UI for building segmentations is hard. But if you can count on high performance processing event level detail in your queries (and by high-performance I mean sub-second – check out my demos if you don’t believe me), you can support really robust segmentation without having to worry about the data engine or the basic performance of queries. That’s a luxury I plan to take full advantage of in delivering a product that segments. And segments. And segments again.

When I describe my new company Digital Mortar to folks, the most common reaction I get is: “Can you really do this?”

Depending on their level of experience in the field, that question has one of two meetings. If they haven’t used existing in-store customer tracking solutions, the question generally means: is the technology practical and is it actually OK to use it (i.e. does it violate privacy policies)? If they have experience with existing in-store customer tracking solutions what they mean is: “does your stuff actually work as opposed to the garbage I’ve been using?”

I’m going to tackle the first question today (is the technology practical and legal) and leave the second for next time.

Is the Technology Practical?

Yes. As my post last week made clear, the various technologies for in-store customer tracking have challenges. Data quality is a real problem. There are issues with positional accuracy, visitorization, journey tracking, and even basic reliability. This is still cutting or even bleeding-edge technology. It’s like digital analytics circa 2005 not digital analytics 2017. But the technologies work. They can be deployed at scale and for a reasonable cost. The data they provide needs careful cleaning and processing. But so does almost any data set. If chosen appropriately and implemented well, the technologies provide data that is immediately valuable and can drive true continuous improvement in stores.

How Hard is it to Deploy In-Store Tracking?

Unfortunately, the in-store customer tracking technologies that don’t take at least some physical in-store installation (Wi-Fi Access Point based measurement and piggybacking off of existing security cameras) are also the least useful. Wi-Fi measurement is practical for arenas, airports, malls and other very large spaces with good Wi-Fi opt-in rates. For stores, it just doesn’t work well enough to support serious measurement. Security cameras can give you inaccurate, zone based counts and not much else. Good in-store measurement will require you install either measurement focused cameras or passive sniffers. Of the two, sniffers are lot easier. You need a lot less of them. The placement is easier. The power and cabling requirements are lower. And they are quite a bit cheaper.

Either way, you should expect that it will take a few weeks to plan out the deployment for a new store layout. This will also involve coordination with your installation partner. Typically, the installation is done over one or two evenings. No special closing is required. With sniffers, the impact on the store environment is minimal. The devices are about the size of a deck of playing cards, can be painted to match the environment and any necessary wiring is usually hidden.

After a couple week shake down, you’ll have useable measurement and a plan you can roll out to other stores. Subsequent stores with the same or similar layout can be done as quickly as your installation partner will schedule them. And the post-install shake-down period is less.

So if you’re planning a Pilot project, here’s the timeline we use at Digital Mortar:

Month 1

Select Store Targets: We typically recommend 3 stores in a Pilot – one test and two control stores with similar layout and market.

Select Initial Store

Design Implementation for the Initial Store

Train Installation Partner

Do initial 1 store installation

Month 2

Test the initial installation and tune plan if necessary

Rollout to additional stores

Provide initial reporting

Targeted analysis to develop store testing plan

Month 3

Run initial test(s)

Analyze control vs. test

Assess findings and make optimization recommendations

Evaluate pilot program

This kind of Pilot timeline gets you live, production data early in Month 2 with initial store findings not long after. And it gets you real experience with the type of analysis, testing and continuous improvement cycle that make for effective business use.

Is it Ok to Use Location Analytics?

Yes. In-store tracking technology is already widely used. The majority of major retailers have tried it in various forms. There is an established community of interest focused on privacy and compliance in location analytics (the Future of Privacy Forum) that is supported by the major technology players (including giants like Cisco who do this routinely), major retailers, most of the vendors specific to the space, and plenty of heavy-hitters from a political standpoint. They’ve published guidelines (with input from the FTC) on how to do this. In many respects, the landscape is similar to digital. To do this right, you must have a documented and published privacy policy and you MUST adhere to your own privacy policy. If you offer an online opt-out, you must provide and honor an online opt-out. If you offer an in-store opt-out, you must provide it. To abide by the privacy standards, you must treat the visitor’s phone MAC address as PII information. You must not keep and match the visitor’s MAC address without opt-in and you should make sure that is hashed or transformed when stored.

And, of course, in the EU the tracking guidelines are significantly more restrictive.

In almost all respects, this is identical to the use of cookies in the digital world. And, as with the digital world, it’s not hard to see where the blurry lines are. Using in-store customer journey tracking to improve the store is non-controversial – the equivalent of using first-party cookies to analyze and improve a website. Using appropriately described opt-ins to track and market to identified customers is fine as long as the usage is appropriately disclosed. Selling customer information begins to touch on gray areas. And identifying and marketing to users without opt-in using any kind of device fingerprinting is very gray indeed.

Bottom line? In-store customer tracking and location analytics is ready for prime-time. The technologies work. They can be deployed reasonably and provide genuinely useful data. Deployment is non-trivial but is far from back-breaking. And the proper uses of the data are understood and widely accepted.

In my next post, I’ll take up the analytic problems that have crippled existing solutions and explain how we’ve solved them.

Store layout, promotion and staff optimization are the immediate and obvious ways to use the core data from customer journey analytics. Together, they comprise the “you” part of the equation – optimizing your operational and marketing strategies. But the uses of in-store tracking don’t end there. There’s tremendous strategic value in being to understand customer journeys – a lesson we’ve learned over and over again in digital. When it comes to omni-channel, store and experience design, and the integration of new technologies to the store, you simply can’t do the job right without in-store journey measurement.

In my last post, I described what in-store customer data is. But the really important question is this – what do you do with it? Not surprisingly, in-store customer movement data serves quite a range of needs that I’ll categorize broadly as store layout optimization, promotion planning and optimization, staff optimization, digital experience integration, omni-channel experience optimization, and customer experience optimization. I’ll talk about each in more detail, but you can think about it this way. Half of the utility of in-store customer journey measurement is focused on you – your store, your promotions and your staff. When you can measure the in-store customer journey better, you can optimize your marketing and operations more effectively. It’s that simple. The other half of the equation is about the customer. Mapping customer segments, finding gaps in the experience, figuring out how omni-channel journeys work. This kind of data may have immediate tactical implications but it’s real function is strategic. When you understand the customer experience better you can design better stores, better marketing campaigns, and better omni-channel strategies.

I’m going to cover each area in a short post, starting with the most basic and straightforward (store layout) and moving up into the increasingly strategic uses.

Store Layout and Merchandising Optimization

While bricks&mortar hasn’t had the kind of measurement and continuous improvement systems that drive digital, it has had a long, arduous and fruitful journey to maturity. Store analysts and manager know a lot. And while in-store customer journey measurement can fill in some pretty important gaps, you can do a lot of good store optimization based on a combination of well-understood best practices, basic door-counting, and PoS information. At a high-level, retailers understand how product placement drives sales, what the value of an end-cap/feature is, and how shelf placement matters. With PoS data, they also understand which products tend to be purchased together. So what’s missing? Quite a bit, actually, and some of it is pretty valuable. With customer journey data you can do true funnel analysis – not just at the store level (PoS/Door Counting) but at a detailed level within the store. You’ll see the opportunity each store area had, what customer segments made up that opportunity, and how well the section of the store is engaging customers and converting on the opportunity. Funnel analysis forever changed the way people optimized websites. It can do the same for the store. When you make a change, you can see how patterns of movement, shopping and segmentation all shift. You can isolate specific segments of customer (first time, regular, committed shopper, browser) and see how their product associations and navigation patterns differ. If this sounds like continuous improvement through testing…well, that’s exactly what it is.

Digital Mortar is committed to bringing a whole new generation of measurement and analytics to the in-store customer journey. What I mean by that “new generation” is that our approach embodies more complete and far more accurate data collection. I mean that it provides far more interesting and directive reports. And I mean that our analytics will make a store (or other physical space) work better. But how does that happen and why do we need to track customers inside the store when we know what they buy? After all, it’s not as if traditional stores are unmeasured. Stores have, at minimum, PoS data and store merchandising and operations data. In other words, we know what we had to sell, we know how many people we used to sell it, and we know how much (and what and what profit) we actually sold.

That stuff is vital and deeply explanatory.

It constitutes the data necessary to optimize assortment, manage (to some extent) staffing needs, allocate staff to areas, and understand which categories are pulling their weight. It can even, with market basket analysis, help us understand which products are associated in customer’s shopping behaviors and can form the basis for layout optimization.

We come from a digital analytics background – analyzing customer experience on eCommerce sites we often had a similar situation. The back-office systems told us which products were purchased, which were bought together, which categories were most successful. You didn’t need a digital analytics solution to tell you any of that. So if you bought, implemented and tried to use a digital analytics solution and those were your questions…well, you were going to be disappointed. Not because a digital analytics solution couldn’t provide answers, it just couldn’t provide better answer than you already had.

It’s the same with in-store tracking systems; which is why when we’re building our system, evaluating reports or doing analysis for clients at Digital Mortar, I find myself using the PoS test. The PoS test is just this pretty simple question: does using the customer in-store journey to answer the question provide better, more useful information than simply knowing what customers bought?

When the answer yes, we build it. But sometimes the answer is no – and we just leave well enough alone.

Let me give you some examples from real-life to show why the PoS test can help clarify what In-Store tracking is for. Here’s three different reports based on understanding the in-store customer journey:

#1: There are regular in-store events hosted by each location. With in-store tracking, we can measure the browsing impact of these events and see if they encourage people to shop products.

#2: There are sometimes significant category performance differences between locations. With in-store tracking, we can measure whether the performance differences are driven by layout, by traffic type, by weather or by area shop per preferences.

#3: Matching staffing levels to store traffic can be tricky. Are there times when a store is understaffed leaving sales, literally, on the table? With in-store tracking we can measure associate / customer rations, interactions and performance and we can identify whether and how often lowered interaction rates lost sales.

I think all three of these reports are potentially interesting – they’re perfectly reasonable to ask for and to produce.

With #1, however, I have to wonder how much value in-store tracking will add beyond PoS data. I can just as easily correlate PoS data to event times to see if events drive additional sales. What I don’t know is whether event attendees browse but don’t buy. If I do this analysis with in-store tracking data, the first question I’ll get is “But did they buy anything?” If, on the other hand, I do the analysis with PoS data, I’m much less likely to hear “But did they browse the store?” So while in-store tracking adds a little bit of information to the problem, it’s probably not the best or the easiest way to understand the impact of store events. We chose not to include this type of report in our base report set, even though we do let people integrate and view this type of data.

Question #2 is quite different. The question starts with sales data. We see differences in category sales by store. So more PoS data isn’t going to help. When you want to know why sales are different (by day, by store, by region, etc.), then you’ll need other types of data. Obviously, you’ll need square footage to understand efficiency, but the type of store layout data you can bring to bear is probably even more critical than measures of efficiency. With in-store tracking you can see how often a category functions as a draw (where customers go first), how it gets traffic from associated areas, how much opportunity it had, and how well it actually performed. Along with weather and associate interaction data, you have almost every factor you’re likely to need to really understand the drivers of performance. We made sure this kind of analytics is easy in our tool. Not just by integrating PoS data, but by making sure that it’s possible to understand and compare how store layouts shape category browsing and buying.

Question #3 is somewhere in between. By matching staffing data to PoS data, I can see if there are times when I look understaffed. But I’m missing significant pieces of information if I try to optimize staff using only PoS data. Door-counting data can take this one step further and help me understand when interaction opportunities were highest (and most underserved). With full in-store journey tracking, I can refine my answers to individual categories / departments and make sure I’m evaluating real opportunities not, for example, mall pass throughs. So in-store journey tracking deepens and sharpens the answer to Staffing Gaps well beyond what can be achieved with only PoS data or even PoS and door-counting data. Once again, we chose to include staff optimization reports (actually a whole bunch of them) in the base product. Even though you can do interesting analysis with just PoS data, there’s too much missing to make decision-makers informed and confident enough to make changes. And making changes is what it’s all about.

We all know the old saying about everything looking like a nail when your only tool is a hammer. But the truth is that we often fixate on a particular tool even when many others are near to hand. You can answer all sorts of questions with in-store journey tracking data, but some of those questions can be answered as well or better using your existing PoS or door-counting data. This sort of analytics duplication isn’t unique to in-store tracking. It’s ubiquitous in data analytics in general. Before you start buying systems, using reports or delving into a tool, it’s almost always worth asking if it’s the right/easiest/best data for the job. It just so happens that with in-store tracking data, asking how and whether it extends PoS data is almost always a good place to start.

In creating the DM tool, we’ve tried to do a lot of that work for you. And by applying the PoS test, we think we’ve created a report set that helps guide you to the best uses of in-store tracking data. The uses that take full advantage of what makes this data unique and that don’t waste your time with stuff you already (should) know.

People have struggled with this (big) data provider model but Factual feels like it’s found a real (and valuable) niche. Would love to see more of this grow since external data is a huge miss in most big data systems.

Targeted VoC is a powerful (and totally neglected) tool for personalization. Facebook’s experience is entirely relevant to ANY content producer. I don’t know if I can take credit for this, but I suggested this to folks at Facebook a couple of years back!

An interesting discussion of the problems in identifying “likely” voters and the benefits of behavioral data integration. Food for thought in the enterprise world as well where the equivalent is often possible but rarely done.